Design and Evaluation of an Intelligent Remote Tidal Volume Variability Monitoring System in E-Health Applications
Bibliographic record
Abstract
A reliable long-term monitoring and diagnosis of breath disorders at an early stage provides an improvement of medical act, life expectancy, and quality of life while decreasing the costs of treatment and medical services. Therefore, a real-time unobtrusive monitoring of respiration patterns, as well as breath parameters, is a critical need in medical applications. In this paper, we propose an intelligent system for patient home care, capable of measuring respiration rate and tidal volume variability via a wearable sensing technology. The proposed system is designed particularly for the goal of diagnosis and treatment in patients with pathological breathing, e.g., respiratory complications after surgery or sleep disorders. The complete system was comprised of wearable calibrated accelerometer sensor, Bluetooth low energy, and cloud database. The experiments are conducted with eight subjects and the overall error in respiration rate calculation is obtained 0.29%±0.33% considering SPR-BTA spirometer as the reference. We also introduce a method for tidal volume variability estimation while validated using Pearson correlation. Furthermore, since it is essential to detect the critical events resulted from sudden rise or fall in per breath tidal volume of the patients, we provide a technique to automatically find the accurate threshold values based on each individual breath characteristics. Therefore, the system is able to detect the major changes, precisely by more than 98%, and provide immediate feedback such as sound alarm for round-the-clock respiration monitoring.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".